



'''simple docstring'''


import numpy




class   __UpperCamelCase							:







	def __init__(     self			,					_lowerCAmelCase			,					_lowerCAmelCase    )     ->     None:
							'''simple docstring'''






							lowercase   =				input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							lowercase   =				numpy.random.rand(
							    self.input_array.shape[1]			,					4    )

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							lowercase   =				numpy.random.rand(
							    4			,					3    )

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							lowercase   =				numpy.random.rand(3			,					1    )

							# Real output values provided.
							lowercase   =				output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							lowercase   =				numpy.zeros(output_array.shape    )







	def 	_a (     self    )     ->     numpy.ndarray:
							'''simple docstring'''






							lowercase   =				sigmoid(
							    numpy.dot(self.input_array			,					self.input_layer_and_first_hidden_layer_weights    )    )

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							lowercase   =				sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer			,					self.first_hidden_layer_and_second_hidden_layer_weights			,					)    )

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							lowercase   =				sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer			,					self.second_hidden_layer_and_output_layer_weights			,					)    )

							return self.layer_between_second_hidden_layer_and_output







	def 	_a (     self    )     ->     None:
							'''simple docstring'''






							lowercase   =				numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T			,					2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output    )			,					)
							lowercase   =				numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T			,					numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output    )			,					self.second_hidden_layer_and_output_layer_weights.T			,					)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer    )			,					)
							lowercase   =				numpy.dot(
							    self.input_array.T			,					numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output    )			,					self.second_hidden_layer_and_output_layer_weights.T			,					)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer    )			,					self.first_hidden_layer_and_second_hidden_layer_weights.T			,					)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer    )			,					)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)







	def 	_a (     self			,					_lowerCAmelCase			,					_lowerCAmelCase			,					_lowerCAmelCase    )     ->     None:
							'''simple docstring'''






							for iteration in range(1			,					iterations + 1    ):
													lowercase   =				self.feedforward()
													self.back_propagation()
													if give_loss:
																			lowercase   =				numpy.mean(numpy.square(output - self.feedforward()    )    )
																			print(F"""Iteration {iteration} Loss: {loss}"""    )







	def 	_a (     self			,					_lowerCAmelCase    )     ->     int:
							'''simple docstring'''






							lowercase   =				input_arr

							lowercase   =				sigmoid(
							    numpy.dot(self.array			,					self.input_layer_and_first_hidden_layer_weights    )    )

							lowercase   =				sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer			,					self.first_hidden_layer_and_second_hidden_layer_weights			,					)    )

							lowercase   =				sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer			,					self.second_hidden_layer_and_output_layer_weights			,					)    )

							return int(self.layer_between_second_hidden_layer_and_output > 0.6    )







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   numpy.ndarray    ):
						return 1 / (1 + numpy.exp(-value    ))







def   SCREAMING_SNAKE_CASE					(					lowercase_     :   numpy.ndarray    ):
						return (value) * (1 - (value))







def   SCREAMING_SNAKE_CASE					(					):
						lowercase   =				numpy.array(
						    (
						        [0, 0, 0],
						        [0, 0, 1],
						        [0, 1, 0],
						        [0, 1, 1],
						        [1, 0, 0],
						        [1, 0, 1],
						        [1, 1, 0],
						        [1, 1, 1],
						    )			,	dtype=numpy.floataa			,	)

						# True output values for the given input values.
						lowercase   =				numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])			,	dtype=numpy.floataa    )

						# Calling neural network class.
						lowercase   =				TwoHiddenLayerNeuralNetwork(
						    input_array=lowercase_			,	output_array=lowercase_    )

						# Calling training function.
						# Set give_loss to True if you want to see loss in every iteration.
						neural_network.train(output=lowercase_			,	iterations=10			,	give_loss=lowercase_    )

						return neural_network.predict(numpy.array(([1, 1, 1])			,	dtype=numpy.floataa    )    )


if __name__ == "__main__":
						example()



A						=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A						=			[{'type': 'code', 'content': INSTALL_CONTENT}]
A						=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}